This report examines pre-VR conditions and side effect severity. Analyses include a scatter plot showing a positive relation between VR anxiety and severity, double box plots comparing state anxiety and severity, and a comparative box plot linking expectancy to severity. Findings suggest anxiety and expectancy positively relate to side effects.
Exploratory Data Analysis (EDA)
Structures
The data for this project comes from Saunders et al.’s 2023 research report. It includes data from 336 participants and 82 different variables.
Quantitative Variables
p_vra (Discrete) : VR anxiety from 0 to 10.
expect (Discrete) : Expectancy score.
ssq_full (Discrete) : Side effect severity calculated from ASSQ minus BSSQ.
Qualitative Variables
The following below variables represent active state anxiety index on a 4-point Likert scale (“Very Much”, “Moderate”, “Somewhat”, “Not at all”), which is an ordinal type.
PSTAI1 : Calm
PSTAI2 : Tense
PSTAI4 : Relaxed
PSTAI6 : Worried
Limitations
The study was single-blind, which may have caused observer bias. It was conducted via Zoom, so results depended on participants’ internet connection. Data were collected in 2021 during the COVID-19 pandemic, which may have influenced side effects.
Assumptions
We assumed the variables (PSTAI, p_e, and p_vra) accurately measure anxiety and that participants understood and completed the survey correctly.
Data Cleaning & Transformation
PSTAI responses were aggregated into positive (1: “Very much”, “Somewhat”, “Moderately”) and negative (0: “Not at all”). Expectancy was split into Low/High by the median. These transformations are for easier analysis in identifying anxiety and non-anxiety groups.
figure_1 <-ggplot(pooled_data, aes(x = p_vra, y = ssq_full)) +geom_point() +geom_smooth(method ="lm", formula = y ~ x, se =FALSE) +labs(title ="Side Effect Severity vs VR Anxiety",x ="VR Anxiety",y ="Side Effect Severity")ggplotly(figure_1)
Code
model =lm(ssq_full ~ p_vra, data = pooled_data)figure_2 <-ggplot(model, aes(x = .fitted, y = .resid)) +geom_point() +geom_hline(yintercept =0, linetype ="dashed", colour ="red") +labs(title ="Residual Plot")ggplotly(figure_2)
Code
summary(model)
Call:
lm(formula = ssq_full ~ p_vra, data = pooled_data)
Residuals:
Min 1Q Median 3Q Max
-32.977 -10.385 -3.590 7.204 86.410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.7997 1.4447 2.630 0.00893 **
p_vra 2.5968 0.3378 7.687 1.7e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15.75 on 334 degrees of freedom
Multiple R-squared: 0.1503, Adjusted R-squared: 0.1478
F-statistic: 59.09 on 1 and 334 DF, p-value: 1.697e-13
The correlation between VR anxiety and side effect severity shows \(0.388\), indicating a low positive correlation. Figure 1 supports a linear relationship between two variables. Figure 2 reinforces the relationship by showing approximately random and the same variances in the vertical direction along the fitted axis (homoscedasticity). In conclusion, a linear model between those two variables is appropriate.
Given that the assumptions of the linear model are satisfied, performing a regression test is appropriate. The regression results in the summary statistics show a p-value of\(1.7 \times 10^{-13}\). Assuming a significance level of \(0.05\), we reject the null hypothesis (\(H_0: \text{slope} = 0\)), indicating that the alternative hypothesis (\(H_1: \text{slope} \neq 0\)) is supported. This suggests a significant linear relationship between VR anxiety and side effect severity. The relationship is described by the following regression equation:
ggplot(pooled_melt, aes(x = Label, y = ssq_full, fill = Binary)) +geom_boxplot(position =position_dodge(width =0.8), width =0.6, outlier.shape =NA) +scale_fill_manual(values =c("Positive"="#377eb8", "Negative"="#e41a1c")) +labs(title ="Distribution of Side Effect Severity Scores by PSTAI Group",x ="PSTAI Variable",y ="Side Effect Severity",fill ="PSTAI Group") +theme_minimal()
The above comparative bar plot showed that side effect severity aligned with anxiety, as indicated by higher reports of tension and worry and lower reports of relaxation and calmness, consistent with established markers of anxiety (American Psychiatric Association, 2022).
This is evidenced by the median scores of the side effect severity, where it reveals higher median “Negative” scores for Calm and Relaxed, and higher median “Positive” scores for Tension and Worry.
Code
p <-ggplot(pooled_data, aes(x = expect_group, y = ssq_full, fill = expect_group)) +geom_boxplot() +labs(title ="Side Effect Severity by Expectancy Group",x ="Expectancy Group",y ="Side Effect Severity" ) +theme(plot.title =element_text(hjust =0.5) )interactive_plot <-ggplotly(p)interactive_plot
When looking at the box plot above, the “High” expectancy group median generally shows higher severity outcomes compared to the “Low” expectancy group median. The “High” group also has a wider IQR, pointing to greater variation in symptom severity among those with higher expectancy. Expectancy was divided by whether scores were above or below the overall median. One noticeable feature is an outlier in the Low group, which may explain why the difference between the two groups was less clear in the first analysis.
Taken together, these findings suggest that higher expectancy is associated with stronger VR symptom reporting and greater variability, possibly reflecting increased anxiety in those with high or negative expectancy (Steinman et al., 2013), leading to more severe side effects.
Ethics & Standards
We adhere to the shared professional values of “Truthfulness and Integrity” in our report by demonstrating transparency in the statistical methodologies used and making these methodologies publicly available. We also adhere to “Pursuing Objectivity” by clearly stating the limitations of our findings and employing methods to produce the best possible results, while maintaining open, complete, and transparent outcomes.
Acknowledgments
Group Meetings
Date
Time
Attendance
Friday, 12 September 2025
18:30 - 20:30
Kenneth Davis
Edward Danar Atmojo
Rickey Arvidson
Leo Trimble
Yuexi Luo
Friday, 19 September 2025
18:00 - 19:00
Kenneth Davis
Rickey Arvidson
Leo Trimble
Yuexi Luo
Contributions
Group Member
Contribution
Kenneth Davis
Analysis (VR Anxiety and Side Effect Severity Relationship)
Edward Danar Atmojo
Analysis (PSTAI and Side Effect Severity Relationship)
Rickey
Analysis (Expectancy Score and Side Effect Severity Relationship)
R Core Team. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Wickham, Hadley, Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., Woo, K., Yutani, H., Dunnington, D., & van den Brand, T. (2025). A box and whiskers plot (in the style of Tukey) - geom_boxplot. - geom_boxplot • ggplot2. https://ggplot2.tidyverse.org/reference/geom_boxplot.html
Quarto - HTML Basics. (n.d.). Quarto.org. https://quarto.org/docs/output-formats/html-basics.html
The logic of data transformation using melt was suggested during drop in session (Han, Wednesday Drop In Session Tutor , September 17, 2025).
Articles
American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). Arlington, VA: American Psychiatric Publishing.
Steinman, S. A., Smyth, F. L., Bucks, R. S., MacLeod, C., & Teachman, B. A. (2013). Anxiety-linked expectancy bias across the adult lifespan. Cognition & Emotion, 27(2), 345–355. https://doi.org/10.1080/02699931.2012.711743
Centers for Disease Control and Prevention. (2024, June 25). Symptoms of COVID-19. COVID-19; CDC. https://www.cdc.gov/covid/signs-symptoms/index.html
Saunders, C., Colagiuri, B., & Barnes, K. (2023). Socially acquired nocebo effects generalize but are not attenuated by choice. Annals of Behavioral Medicine, 57(12), 1069–1080.https://doi.org/10.1093/abm/kaad056↩︎
